{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append('../')\n",
    "\n",
    "import datasets\n",
    "import pandas as pd\n",
    "import glob\n",
    "import string\n",
    "from collections import Counter\n",
    "from panlex_utils import load_panlex_resources, extract_monolingual_lexicon\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing bug bug\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.bug-date=20221220,language=bug/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bb6106a3b3e48d69f3227a714a2d564",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing mad mad\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.mad-date=20221220,language=mad/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "463f741d72da48b8b1830943c029fd20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing jv jav\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.jv-date=20221220,language=jv/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "853abba0ddfb4267a976b59ee38a2699",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing min min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.min-date=20221220,language=min/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b99f88ad83c34482971412c198d99221",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing su sun\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.su-date=20221220,language=su/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45089c8d396244e7a660288f0c8f9a85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing map-bms map-bms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.map-bms-date=20221220,language=map-bms/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f260fec90f24b089eead09db91a3ab0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing ace ace\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.ace-date=20221220,language=ace/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a16f28bfa9e4285aaa2d6175690558e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing gor gor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.gor-date=20221220,language=gor/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e466f1479782441ba3df88e3f0c6aace",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing ban ban\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.ban-date=20221220,language=ban/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44c8442922e74724a727b330d8ba5c97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing bjn bjn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.bjn-date=20221220,language=bjn/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32d4a6e36380448abe357140fdf5ee97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing nia nia\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.nia-date=20221220,language=nia/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6bc481f315d4c5289bf79b979bcea43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing id id\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.id-date=20221220,language=id/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "751d5b8f3ac74223b53e78ea9106966e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load Wiki\n",
    "wiki_dsets = {}\n",
    "for lang in ['bug', 'mad', 'jv', 'min', 'su', 'map-bms', 'ace', 'gor', 'ban', 'bjn', 'nia', 'id']:\n",
    "    if lang == 'su':\n",
    "        iso_lang = 'sun'\n",
    "    elif lang == 'jv':\n",
    "        iso_lang = 'jav'\n",
    "    else:\n",
    "        iso_lang = lang\n",
    "    print(f'processing {lang} {iso_lang}')\n",
    "    wiki_dsets[iso_lang] = datasets.load_dataset('olm/wikipedia', language=lang, date=\"20221220\")['train'].to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Paragraph\n",
    "paragraph_dsets = {}\n",
    "for path in glob.glob('../data/nusa_alinea-paragraph-*.csv'):\n",
    "    _, _, lang, _ = path[:-4].split('/')[-1].split('-')\n",
    "    if lang not in ['bug', 'mad', 'jav','min','sun']:\n",
    "        continue\n",
    "\n",
    "    if lang not in paragraph_dsets:\n",
    "        paragraph_dsets[lang] = []\n",
    "    paragraph_dsets[lang].append(pd.read_csv(path))\n",
    "\n",
    "for lang, dfs in paragraph_dsets.items():\n",
    "    paragraph_dsets[lang] = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load MT\n",
    "mt_dsets = {}\n",
    "for path in glob.glob('../data/nusa_kalimat-mt-*.csv'):\n",
    "    _, _, lang, _ = path[:-4].split('/')[-1].split('-')\n",
    "    if lang not in ['bug', 'mad', 'jav','min','sun']:\n",
    "        continue\n",
    "\n",
    "    if lang not in mt_dsets:\n",
    "        mt_dsets[lang] = []\n",
    "    mt_dsets[lang].append(pd.read_csv(path))\n",
    "\n",
    "for lang, dfs in mt_dsets.items():\n",
    "    mt_dsets[lang] = pd.concat(dfs)\n",
    "    mt_dsets[lang]['text'] = mt_dsets[lang]['tgt_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.4 s, sys: 415 ms, total: 27.8 s\n",
      "Wall time: 27.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Chunk & Count Text\n",
    "wiki_counters = {}\n",
    "paragraph_counters = {}\n",
    "mt_counters = {}\n",
    "\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    # Clean Text\n",
    "    replacement_rules = str.maketrans('', '', string.punctuation)\n",
    "    wiki_dsets[lang]['clean_text'] = wiki_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_dsets[lang]['clean_text'] = paragraph_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in mt_dsets:\n",
    "        mt_dsets[lang]['clean_text'] = mt_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "\n",
    "    # Chunk & Count Text\n",
    "    wiki_counter = Counter()\n",
    "    paragraph_counter = Counter()\n",
    "    mt_counter = Counter()\n",
    "    \n",
    "    for ct in wiki_dsets[lang]['clean_text']:\n",
    "        for word in ct.split(' '):\n",
    "            if len(word) > 0:\n",
    "                wiki_counter[word] += 1\n",
    "\n",
    "    if lang in paragraph_dsets:\n",
    "        for ct in paragraph_dsets[lang]['clean_text']:\n",
    "            for word in ct.split(' '):\n",
    "                if len(word) > 0:\n",
    "                    paragraph_counter[word] += 1\n",
    "\n",
    "    if lang in mt_dsets:\n",
    "        for ct in mt_dsets[lang]['clean_text']:\n",
    "            for word in ct.split(' '):\n",
    "                if len(word) > 0:\n",
    "                    mt_counter[word] += 1\n",
    "                    \n",
    "    wiki_counters[lang] = wiki_counter\n",
    "    paragraph_counters[lang] = paragraph_counter\n",
    "    mt_counters[lang] = mt_counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 15866 1000 0\n",
      "mad 1002 5211 9449\n",
      "jav 72737 10188 9449\n",
      "min 226237 8608 9449\n",
      "sun 61331 9594 9449\n"
     ]
    }
   ],
   "source": [
    "# Number of document\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_doc, para_doc, mt_doc = 0, 0, 0\n",
    "    \n",
    "    wiki_doc = len(wiki_dsets[lang])\n",
    "    if lang in paragraph_dsets:\n",
    "        para_doc = len(paragraph_dsets[lang])\n",
    "    if lang in mt_dsets:\n",
    "        mt_doc = len(mt_dsets[lang])\n",
    "    \n",
    "    print(f'{lang} {wiki_doc} {para_doc} {mt_doc}')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 287823 118392 0\n",
      "mad 106335 573833 211503\n",
      "jav 8519436 1116678 208034\n",
      "min 12623423 960961 211084\n",
      "sun 5514173 1111848 209492\n"
     ]
    }
   ],
   "source": [
    "# Number of tokens\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_num, para_num, mt_num = 0, 0, 0\n",
    "    \n",
    "    wiki_num = sum(wiki_counters[lang].values())\n",
    "    if lang in paragraph_dsets:\n",
    "        para_num = sum(paragraph_counters[lang].values())\n",
    "    if lang in mt_dsets:\n",
    "        mt_num = sum(mt_counters[lang].values())\n",
    "    print(f'{lang} {wiki_num} {para_num} {mt_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 18.140867263330392 118.392 0.0\n",
      "mad 106.12275449101796 110.11955478794857 22.383638480262462\n",
      "jav 117.12657932001595 109.60718492343933 22.016509683564397\n",
      "min 55.79734084168372 111.63580390334573 22.339295163509366\n",
      "sun 89.90841499404868 115.88993120700438 22.17081172610858\n"
     ]
    }
   ],
   "source": [
    "# Number of tokens / document\n",
    "min_threshold = 5\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_num, para_num, mt_num = 0, 0, 0\n",
    "    wiki_doc, para_doc, mt_doc = 1, 1, 1\n",
    "    \n",
    "    wiki_num = sum(wiki_counters[lang].values())\n",
    "    wiki_doc = len(wiki_dsets[lang])\n",
    "    if lang in paragraph_dsets:\n",
    "        para_num = sum(paragraph_counters[lang].values())\n",
    "        para_doc = len(paragraph_dsets[lang])\n",
    "    if lang in mt_dsets:\n",
    "        mt_num = sum(mt_counters[lang].values())\n",
    "        mt_doc = len(mt_dsets[lang])\n",
    "    \n",
    "    print(f'{lang} {wiki_num / wiki_doc} {para_num / para_doc} {mt_num / mt_doc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 17542 12909 0\n",
      "mad 23085 42443 34422\n",
      "jav 483499 45190 28707\n",
      "min 284476 46955 26980\n",
      "sun 289629 47648 25831\n"
     ]
    }
   ],
   "source": [
    "# Number of Unique Tokens\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_len, para_len, mt_len = 0, 0, 0\n",
    "    \n",
    "    wiki_len = len(wiki_counters[lang])\n",
    "    if lang in paragraph_dsets:\n",
    "        para_len = len(paragraph_counters[lang])\n",
    "    if lang in mt_dsets:\n",
    "        mt_len = len(mt_counters[lang])\n",
    "    print(f'{lang} {wiki_len} {para_len} {mt_len}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 17542 0.9061967467079783 0.0\n",
      "mad 23085 0.8601451324097634 0.8637538854835429\n",
      "jav 483499 0.410258679825629 0.43667270447262085\n",
      "min 284476 0.4418817616492035 0.4901597420406953\n",
      "sun 289629 0.38248441730151733 0.44758439145246204\n"
     ]
    }
   ],
   "source": [
    "# Percentage of novel words\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_words = set(wiki_counters[lang].keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        para_words = set(paragraph_counters[lang].keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_words = set(mt_counters[lang].keys())\n",
    "\n",
    "    print('{} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(para_words - wiki_words) / (len(para_words) + 1),\n",
    "        len(mt_words - wiki_words) / (len(mt_words) + 1)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 17542 0.9309696175112581 0.9999429972068631\n",
      "mad 23085 0.742874469375379 0.7968465736810187\n",
      "jav 483499 0.9448790072388832 0.9665522233712512\n",
      "min 284476 0.9078765594406578 0.9516445969269923\n",
      "sun 289629 0.8984083140558644 0.9507302420329385\n"
     ]
    }
   ],
   "source": [
    "# Percentage of novel words\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_words = set(wiki_counters[lang].keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        para_words = set(paragraph_counters[lang].keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_words = set(mt_counters[lang].keys())\n",
    "\n",
    "    print('{} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(wiki_words - para_words) / (len(wiki_words) + 1),\n",
    "        len(wiki_words - mt_words) / (len(wiki_words) + 1)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 17542 0.0609469675913058 0.10903516255184006 0.0\n",
      "mad 23085 0.21709486909419198 0.07396389896729716 0.1627486950601407\n",
      "jav 483499 0.05675245911202818 0.040468209754101224 0.13799120340327348\n",
      "min 284476 0.022535565627836 0.04886249404242832 0.1278158087974039\n",
      "sun 289629 0.05252445787891351 0.04285474016705506 0.1233024492465142\n"
     ]
    }
   ],
   "source": [
    "# Percentage of Unique Word\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_words = set(wiki_counters[lang].keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        para_words = set(paragraph_counters[lang].keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_words = set(mt_counters[lang].keys())\n",
    "        \n",
    "    print('{} {} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(wiki_words) / (sum(list(wiki_counters[lang].values())) + 1),\n",
    "        len(para_words) / (sum(list(paragraph_counters[lang].values())) + 1),\n",
    "        len(mt_words) / (sum(list(mt_counters[lang].values())) + 1)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percentage of Unique Word\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    data = {}\n",
    "    data['wiki_top'] = list(map(lambda x: x[0], wiki_counters[lang].most_common(100)))\n",
    "    data['wiki_cnt'] = list(map(lambda x: x[1], wiki_counters[lang].most_common(100)))\n",
    "    if lang in paragraph_dsets:\n",
    "        data['para_top'] = list(map(lambda x: x[0], paragraph_counters[lang].most_common(100)))\n",
    "        data['para_cnt'] = list(map(lambda x: x[1], paragraph_counters[lang].most_common(100)))       \n",
    "    if lang in mt_dsets:\n",
    "        data['mt_top'] = list(map(lambda x: x[0], mt_counters[lang].most_common(100)))\n",
    "        data['mt_cnt'] = list(map(lambda x: x[1], mt_counters[lang].most_common(100)))\n",
    "    pd.DataFrame(data).to_csv(f'{lang}_top100.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics with Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_threshold = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 779 2734 0\n",
      "mad 3081 9803 4840\n",
      "jav 86005 12936 4456\n",
      "min 43036 12418 4199\n",
      "sun 52287 13250 4256\n"
     ]
    }
   ],
   "source": [
    "# Number of Unique Tokens with Threshold\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_len, para_len, mt_len = 0, 0, 0\n",
    "    \n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_len = len(wiki_counter)\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_counter = {x: count for x, count in paragraph_counters[lang].items() if count >= min_threshold}\n",
    "        para_len = len(paragraph_counter)\n",
    "    if lang in mt_dsets:\n",
    "        mt_counter = {x: count for x, count in mt_counters[lang].items() if count >= min_threshold}\n",
    "        mt_len = len(mt_counter)\n",
    "        \n",
    "    print(f'{lang} {wiki_len} {para_len} {mt_len}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 779 0.9425749817117776 0\n",
      "mad 3081 0.8771804549627665 0.8295454545454546\n",
      "jav 86005 0.2972325293753865 0.19008078994614003\n",
      "min 43036 0.31575132871637945 0.2576804000952608\n",
      "sun 52287 0.23977358490566036 0.19666353383458646\n"
     ]
    }
   ],
   "source": [
    "# Percentage of novel words in para / mt\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_words = set(wiki_counter.keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_counter = {x: count for x, count in paragraph_counters[lang].items() if count >= min_threshold}\n",
    "        para_words = set(paragraph_counter.keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_counter = {x: count for x, count in mt_counters[lang].items() if count >= min_threshold}\n",
    "        mt_words = set(mt_counter.keys())\n",
    "\n",
    "    print('{} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(para_words - wiki_words) / len(para_words),\n",
    "        len(mt_words - wiki_words) / len(mt_words) if len(mt_words) > 0 else 0\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 779 0.9425749817117776 0\n",
      "mad 3081 0.8771804549627665 0.8295454545454546\n",
      "jav 86005 0.2972325293753865 0.19008078994614003\n",
      "min 43036 0.31575132871637945 0.2576804000952608\n",
      "sun 52287 0.23977358490566036 0.19666353383458646\n"
     ]
    }
   ],
   "source": [
    "# Percentage of novel words in para / mt with Threshold\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_words = set(wiki_counter.keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_counter = {x: count for x, count in paragraph_counters[lang].items() if count >= min_threshold}\n",
    "        para_words = set(paragraph_counter.keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_counter = {x: count for x, count in mt_counters[lang].items() if count >= min_threshold}\n",
    "        mt_words = set(mt_counter.keys())\n",
    "\n",
    "    print('{} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(para_words - wiki_words) / len(para_words),\n",
    "        len(mt_words - wiki_words) / len(mt_words) if len(mt_words) > 0 else 0\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 779 0.7984595635430038 1.0\n",
      "mad 3081 0.6092177864329763 0.7322297955209348\n",
      "jav 86005 0.8942968432067903 0.9580373234114296\n",
      "min 43036 0.8025606469002695 0.9275722650803978\n",
      "sun 52287 0.8073517317880161 0.9346108975462352\n"
     ]
    }
   ],
   "source": [
    "# Percentage of novel words in wiki with Threshold\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_words = set(wiki_counter.keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_counter = {x: count for x, count in paragraph_counters[lang].items() if count >= min_threshold}\n",
    "        para_words = set(paragraph_counter.keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_counter = {x: count for x, count in mt_counters[lang].items() if count >= min_threshold}\n",
    "        mt_words = set(mt_counter.keys())\n",
    "\n",
    "    print('{} {} {} {}'.format(\n",
    "        lang, len(wiki_words), \n",
    "        len(wiki_words - para_words) / len(wiki_words),\n",
    "        len(wiki_words - mt_words) / len(wiki_words)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Lexicon Overlapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 191 ms, sys: 44.5 ms, total: 235 ms\n",
      "Wall time: 297 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Load Lexicon Indonesia & English\n",
    "if os.path.exists('ind_lexicon.zip') and os.path.exists('eng_lexicon.zip'):\n",
    "    ind_lexicon = pd.read_pickle('ind_lexicon.zip')\n",
    "    eng_lexicon = pd.read_pickle('eng_lexicon.zip')\n",
    "else:\n",
    "    langvar_df, expr_df, deno_df = load_panlex_resources('../resources/panlex-20230501-csv')\n",
    "    \n",
    "    ind_lexicon = extract_monolingual_lexicon('ind', langvar_df, expr_df)\n",
    "    eng_lexicon = extract_monolingual_lexicon('eng', langvar_df, expr_df)\n",
    "    \n",
    "    ind_lexicon.to_pickle('ind_lexicon.zip')\n",
    "    eng_lexicon.to_pickle('eng_lexicon.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_words, eng_words = set(), set()\n",
    "replacement_rules = str.maketrans('', '', string.punctuation + '0123456789')\n",
    "\n",
    "for word in ind_lexicon['ind'].values:\n",
    "    ind_words.add(word.lower().translate(replacement_rules))\n",
    "for word in eng_lexicon['eng'].values:\n",
    "    eng_words.add(word.lower().translate(replacement_rules))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug ind 0.3735558408215661 0.30724213606437456 0\n",
      "bug eng 0.4492939666238768 0.25201170446232624 0\n",
      "mad ind 0.4466082440765985 0.3046006324594512 0.3309917355371901\n",
      "mad eng 0.3635183382018825 0.24920942568601448 0.28987603305785126\n",
      "jav ind 0.23033544561362712 0.41535250463821893 0.4326750448833034\n",
      "jav eng 0.4214987500726702 0.2561842918985776 0.31710053859964094\n",
      "min ind 0.24251789199739754 0.4139958125301981 0.44129554655870445\n",
      "min eng 0.3208708987824147 0.2463359639233371 0.3274589187901881\n",
      "sun ind 0.25975863981486796 0.40211320754716984 0.4429041353383459\n",
      "sun eng 0.4184787805764339 0.2538867924528302 0.29464285714285715\n"
     ]
    }
   ],
   "source": [
    "# Using Unique Token\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_words, para_words, mt_words = set(), set(), set()\n",
    "\n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_words = set(wiki_counter.keys())\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_counter = {x: count for x, count in paragraph_counters[lang].items() if count >= min_threshold}\n",
    "        para_words = set(paragraph_counter.keys())\n",
    "    if lang in mt_dsets:\n",
    "        mt_counter = {x: count for x, count in mt_counters[lang].items() if count >= min_threshold}\n",
    "        mt_words = set(mt_counter.keys())\n",
    "\n",
    "    print('{} {} {} {} {}'.format(\n",
    "        lang, 'ind',\n",
    "        len(wiki_words.intersection(ind_words)) / len(wiki_words),\n",
    "        len(para_words.intersection(ind_words)) / len(para_words),\n",
    "        len(mt_words.intersection(ind_words)) / len(mt_words) if len(mt_words) > 0 else 0\n",
    "    ))\n",
    "\n",
    "    print('{} {} {} {} {}'.format(\n",
    "        lang, 'eng',\n",
    "        len(wiki_words.intersection(eng_words)) / len(wiki_words),\n",
    "        len(para_words.intersection(eng_words)) / len(para_words),\n",
    "        len(mt_words.intersection(eng_words)) / len(mt_words) if len(mt_words) > 0 else 0\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug ind 0.4282388829245752 0.4031691330495304 0\n",
      "bug eng 0.48837653696890104 0.4673964457057909 0\n",
      "mad ind 0.3396341750129308 0.37446434764121267 0.3650917481075919\n",
      "mad eng 0.3292518926035642 0.4142407285743413 0.41823993040287843\n",
      "jav ind 0.4258886386375812 0.4888284715916316 0.4385677341203842\n",
      "jav eng 0.43646962075893286 0.46162815063966517 0.46435678783275813\n",
      "min ind 0.4827741255283927 0.5298643753492598 0.4850628185935457\n",
      "min eng 0.5254341869079409 0.4516489222767625 0.4933865191108753\n",
      "sun ind 0.4182511139929777 0.47324454421827444 0.4246701544689057\n",
      "sun eng 0.4742462741013022 0.4221674185680057 0.4319496687224333\n"
     ]
    }
   ],
   "source": [
    "# Using Count\n",
    "for lang in ['bug', 'mad', 'jav','min','sun']:\n",
    "    wiki_word_cnt = sum(wiki_counters[lang].values())\n",
    "    para_word_cnt = sum(paragraph_counters[lang].values())\n",
    "    mt_word_cnt = sum(mt_counters[lang].values())\n",
    "\n",
    "    wiki_ind_cnt, wiki_eng_cnt = 0, 0\n",
    "    para_ind_cnt, para_eng_cnt = 0, 0\n",
    "    mt_ind_cnt, mt_eng_cnt = 0, 0\n",
    "    \n",
    "    for word in ind_words:\n",
    "        wiki_ind_cnt += wiki_counters[lang][word]\n",
    "        para_ind_cnt += paragraph_counters[lang][word]\n",
    "        mt_ind_cnt += mt_counters[lang][word]\n",
    "        \n",
    "    for word in eng_words:\n",
    "        wiki_eng_cnt += wiki_counters[lang][word]\n",
    "        para_eng_cnt += paragraph_counters[lang][word]\n",
    "        mt_eng_cnt += mt_counters[lang][word]\n",
    "        \n",
    "    print('{} {} {} {} {}'.format(\n",
    "        lang, 'ind',\n",
    "        wiki_ind_cnt / wiki_word_cnt,\n",
    "        para_ind_cnt / para_word_cnt,\n",
    "        mt_ind_cnt / mt_word_cnt if mt_word_cnt > 0 else 0,\n",
    "    ))\n",
    "\n",
    "    print('{} {} {} {} {}'.format(\n",
    "        lang, 'eng',\n",
    "        wiki_eng_cnt / wiki_word_cnt,\n",
    "        para_eng_cnt / para_word_cnt,\n",
    "        mt_eng_cnt / mt_word_cnt if mt_word_cnt > 0 else 0,\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wikipedia words CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing bug bug\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.bug-date=20221220,language=bug/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
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       "model_id": "9cd6c7d693cf4451bc044a0920be4658",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing mad mad\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.mad-date=20221220,language=mad/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
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       "model_id": "f5cea6d326644104bfbbbb2a5c6ec617",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing jv jav\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.jv-date=20221220,language=jv/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
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       "model_id": "dea8d886bd88450984c9e3550cf77fb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing min min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.min-date=20221220,language=min/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
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       "model_id": "67f0dd5d5d2b4ef091ddc514f5260aad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing su sun\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.su-date=20221220,language=su/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
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       "model_id": "7a32bdae092f45dfb58dc285982114b8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing map-bms map-bms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.map-bms-date=20221220,language=map-bms/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fdb507146814f5c8a887f0bdc058462",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing ace ace\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.ace-date=20221220,language=ace/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a52843d3ad634780ad70b66e6b4b3e5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing gor gor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.gor-date=20221220,language=gor/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16eff43c40bf43bfb3bb789e4aec3f1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing ban ban\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.ban-date=20221220,language=ban/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7660e626312469f8bab4ab85c941116",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing bjn bjn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.bjn-date=20221220,language=bjn/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53f80f17fd8840b581e682bdc0e1ca56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing nia nia\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.nia-date=20221220,language=nia/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f010967b8a974a48a18c16efdb5ba9aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing id ind\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wikipedia (/home/samuel/.cache/huggingface/datasets/olm___wikipedia/20221220.id-date=20221220,language=id/2.0.0/dbfec0358f063ec7ae9e247d6559e2e505fbce7463e666024718863cbf199ec6)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2370b55638a64bd18c46da242b6b3bd9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load Wiki\n",
    "wiki_dsets = {}\n",
    "for lang in ['bug', 'mad', 'jv', 'min', 'su', 'map-bms', 'ace', 'gor', 'ban', 'bjn', 'nia', 'id']:\n",
    "    if lang == 'su':\n",
    "        iso_lang = 'sun'\n",
    "    elif lang == 'jv':\n",
    "        iso_lang = 'jav'\n",
    "    elif lang == 'id':\n",
    "        iso_lang = 'ind'\n",
    "    elif lang == 'en':\n",
    "        iso_lang = 'eng'\n",
    "    else:\n",
    "        iso_lang = lang\n",
    "    print(f'processing {lang} {iso_lang}')\n",
    "    wiki_dsets[iso_lang] = datasets.load_dataset('olm/wikipedia', language=lang, date=\"20221220\")['train'].to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [03:07<00:00, 15.63s/it]\n"
     ]
    }
   ],
   "source": [
    "lang2size = {\n",
    "    'ind': 510393, 'min': 224181, 'jav': 65011, 'sun': 61179, 'bug': 14407, 'map-bms': 13950, \n",
    "    'ace': 12623, 'gor': 12843, 'ban': 9948, 'bjn': 4587, 'nia': 1073, 'mad': 905\n",
    "}\n",
    "\n",
    "dfs = []\n",
    "wiki_counters = {}\n",
    "for lang, dset in tqdm(wiki_dsets.items()):\n",
    "    # Clean Text\n",
    "    replacement_rules = str.maketrans('', '', string.punctuation + '0123456789')\n",
    "    dset['clean_text'] = dset['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "        \n",
    "    # Chunk & Count Text\n",
    "    wiki_counter = Counter()    \n",
    "    for ct in wiki_dsets[lang]['clean_text']:\n",
    "        for word in ct.split(' '):\n",
    "            if len(word) > 0:\n",
    "                wiki_counter[word] += 1\n",
    "    df = pd.DataFrame({'word': list(wiki_counter.keys()), 'count': list(wiki_counter.values())})\n",
    "    df = df.sort_values('count', ascending=False).reset_index(drop=True).reset_index(drop=False)\n",
    "    \n",
    "    df['lang'] = lang\n",
    "    df['size'] = lang2size[lang]\n",
    "    df['count_norm'] = df['count'] / df['count'].sum()\n",
    "    df['cum_count'] = df['count'].cumsum()\n",
    "    df['cum_count_norm'] = df['count_norm'].cumsum()\n",
    "    df['norm_index'] = df['index'] / len(df)\n",
    "    \n",
    "    wiki_counters[lang] = wiki_counter\n",
    "    dfs.append(df)\n",
    "wiki_df = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### General Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug 15866 287094 18.094919954619943 17204 1501 752\n",
      "mad 1002 100097 99.89720558882236 21862 5204 2925\n",
      "jav 72737 8216771 112.96549211542956 440360 123942 82605\n",
      "min 226237 12177409 53.82589496855068 201341 63947 40423\n",
      "sun 61331 5337682 87.03073486491334 241285 72102 48613\n",
      "map-bms 13575 530811 39.102099447513815 52761 15750 10013\n",
      "ace 12829 477213 37.19798893132746 30020 7516 4493\n",
      "gor 14389 581647 40.42303148238238 41759 10765 6786\n",
      "ban 17161 1640609 95.60101392692734 124511 32533 19955\n",
      "bjn 10122 661857 65.38796680497926 77502 20191 12556\n",
      "nia 1620 242024 149.39753086419753 23660 7168 4505\n",
      "ind 633703 128874702 203.36766908157293 2151394 639134 420609\n"
     ]
    }
   ],
   "source": [
    "# Number of document\n",
    "for lang in list(wiki_dsets.keys()):\n",
    "    wiki_doc = len(wiki_dsets[lang])\n",
    "    wiki_num = sum(wiki_counters[lang].values()) \n",
    "    wiki_unique = len(wiki_counters.keys())\n",
    "    \n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= 1}\n",
    "    wiki_len_1 = len(wiki_counter)\n",
    "    \n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= 3}\n",
    "    wiki_len_3 = len(wiki_counter)\n",
    "    \n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= 5}\n",
    "    wiki_len_5 = len(wiki_counter)\n",
    "    print(f'{lang} {wiki_doc} {wiki_num} {wiki_num / wiki_doc} {wiki_len_1} {wiki_len_3} {wiki_len_5}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Token Distribution\n",
    "Showing commonly repetitive words per language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for lang, value in wiki_df.groupby('lang').head(1000).groupby('lang')['count_norm'].sum().iteritems():\n",
    "    wiki_df.loc[wiki_df['lang'] == lang, 'neg_cum_val'] = -value\n",
    "wiki_df = wiki_df.sort_values(['neg_cum_val', 'index']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_df['size'] = wiki_df['lang'].apply(lambda x: lang2size[x])\n",
    "wiki_df = wiki_df.sort_values(['size', 'index']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "ax = sns.lineplot(wiki_df.loc[wiki_df['index'] < 2000,:], x='index', y='cum_count_norm', hue='lang', palette='crest')\n",
    "ax.axvline(x = 500, ymin = 0, ymax = 1, linestyle='dashed')\n",
    "plt.xticks([0, 500, 1000, 1500, 2000], [x * 500 for x in range(0,5)], fontsize=22)\n",
    "plt.yticks(np.arange(0.1,1.01,0.1), [f'{x}0%' for x in range(1,11)], fontsize=22)\n",
    "plt.legend(ncol=2, fontsize=21)\n",
    "plt.xlabel('top-k words', fontsize=24)\n",
    "plt.ylabel('cumulative count (%)', fontsize=24)\n",
    "plt.savefig('cumulative_count_wiki.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "torch.save(wiki_counters, 'wiki_counter.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71M\twiki_counter.pt\n"
     ]
    }
   ],
   "source": [
    "wiki_counters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Language Intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug ind 0.3896276595744681\n",
      "bug eng 0.46808510638297873\n",
      "mad ind 0.4735042735042735\n",
      "mad eng 0.38564102564102565\n",
      "jav ind 0.24015495430058714\n",
      "jav eng 0.4406391864899219\n",
      "min ind 0.2585409296687529\n",
      "min eng 0.3445810553397818\n",
      "sun ind 0.27986341102174317\n",
      "sun eng 0.45362351634336495\n",
      "map-bms ind 0.589933086986917\n",
      "map-bms eng 0.3586337760910816\n",
      "ace ind 0.38014689517026484\n",
      "ace eng 0.39884264411306475\n",
      "gor ind 0.5841438255231358\n",
      "gor eng 0.40318302387267907\n",
      "ban ind 0.311951891756452\n",
      "ban eng 0.33625657729892255\n",
      "bjn ind 0.49776999044281617\n",
      "bjn eng 0.3861898693851545\n",
      "nia ind 0.2763596004439512\n",
      "nia eng 0.2672586015538291\n",
      "ind ind 0.08504810881364878\n",
      "ind eng 0.41789167611724903\n"
     ]
    }
   ],
   "source": [
    "for lang in list(wiki_dsets.keys()):\n",
    "    wiki_words = set()\n",
    "    wiki_counter = {x: count for x, count in wiki_counters[lang].items() if count >= min_threshold}\n",
    "    wiki_words = set(wiki_counter.keys())\n",
    "\n",
    "    print('{} {} {}'.format(\n",
    "        lang, 'ind',\n",
    "        len(wiki_words.intersection(ind_words)) / len(wiki_words) if len(wiki_words) > 0 else 0,\n",
    "    ))\n",
    "\n",
    "    print('{} {} {}'.format(\n",
    "        lang, 'eng',\n",
    "        len(wiki_words.intersection(eng_words)) / len(wiki_words) if len(wiki_words) > 0 else 0,\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug ind 0.42948302646519954\n",
      "bug eng 0.4898116993040607\n",
      "mad ind 0.36554542094168657\n",
      "mad eng 0.35467596431461484\n",
      "jav ind 0.4451523597286574\n",
      "jav eng 0.45681095408403133\n",
      "min ind 0.5026578313991096\n",
      "min eng 0.5501212942753257\n",
      "sun ind 0.4365986958383808\n",
      "sun eng 0.496654165609716\n",
      "map-bms ind 0.6491613775901404\n",
      "map-bms eng 0.5276360135716855\n",
      "ace ind 0.3399132043762429\n",
      "ace eng 0.46744954559075297\n",
      "gor ind 0.6552359076897156\n",
      "gor eng 0.6443427027045614\n",
      "ban ind 0.44157139208671903\n",
      "ban eng 0.4196112541135639\n",
      "bjn ind 0.5673053242618874\n",
      "bjn eng 0.5104576970554062\n",
      "nia ind 0.3759875053713681\n",
      "nia eng 0.43443212243413876\n",
      "ind ind 0.8109267635784717\n",
      "ind eng 0.5421973274475544\n"
     ]
    }
   ],
   "source": [
    "# Using Count\n",
    "for lang in list(wiki_dsets.keys()):\n",
    "    wiki_word_cnt = sum(wiki_counters[lang].values())\n",
    "    wiki_ind_cnt, wiki_eng_cnt = 0, 0\n",
    "    for word in ind_words:\n",
    "        wiki_ind_cnt += wiki_counters[lang][word]        \n",
    "    for word in eng_words:\n",
    "        wiki_eng_cnt += wiki_counters[lang][word]\n",
    "        \n",
    "    print('{} {} {}'.format(\n",
    "        lang, 'ind',\n",
    "        wiki_ind_cnt / wiki_word_cnt,\n",
    "    ))\n",
    "\n",
    "    print('{} {} {}'.format(\n",
    "        lang, 'eng',\n",
    "        wiki_eng_cnt / wiki_word_cnt,\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Languge Intersection Word Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 248 ms, sys: 32.2 ms, total: 280 ms\n",
      "Wall time: 805 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Load Lexicon Indonesia & English\n",
    "if os.path.exists('ind_lexicon.zip') and os.path.exists('eng_lexicon.zip'):\n",
    "    ind_lexicon = pd.read_pickle('ind_lexicon.zip')\n",
    "    eng_lexicon = pd.read_pickle('eng_lexicon.zip')\n",
    "else:\n",
    "    langvar_df, expr_df, deno_df = load_panlex_resources('../resources/panlex-20230501-csv')\n",
    "    \n",
    "    ind_lexicon = extract_monolingual_lexicon('ind', langvar_df, expr_df)\n",
    "    eng_lexicon = extract_monolingual_lexicon('eng', langvar_df, expr_df)\n",
    "    \n",
    "    ind_lexicon.to_pickle('ind_lexicon.zip')\n",
    "    eng_lexicon.to_pickle('eng_lexicon.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_words, eng_words = set(), set()\n",
    "replacement_rules = str.maketrans('', '', string.punctuation + '0123456789')\n",
    "\n",
    "for word in ind_lexicon['ind'].values:\n",
    "    ind_words.add(word.lower().translate(replacement_rules))\n",
    "for word in eng_lexicon['eng'].values:\n",
    "    eng_words.add(word.lower().translate(replacement_rules))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Count\n",
    "wiki_ind_counters = {}\n",
    "wiki_eng_counters = {}\n",
    "for lang in list(wiki_dsets.keys()):\n",
    "    wiki_ind_counters[lang] = Counter()\n",
    "    wiki_eng_counters[lang] = Counter()\n",
    "    for word in ind_words:\n",
    "        wiki_ind_counters[lang][word] = wiki_counters[lang][word]\n",
    "    for word in eng_words:\n",
    "        wiki_eng_counters[lang][word] = wiki_counters[lang][word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "for lang in list(wiki_dsets.keys()):    \n",
    "    data = {'word':[], 'count':[]}\n",
    "    for w, c in wiki_ind_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "    pd.DataFrame(data).to_csv(f'./wiki_counts/{lang}_ind_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "for lang in list(wiki_dsets.keys()):    \n",
    "    data = {'word':[], 'count':[]}\n",
    "    for w, c in wiki_eng_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "    pd.DataFrame(data).to_csv(f'./wiki_counts/{lang}_eng_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Paragraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Count\n",
    "para_ind_counters = {}\n",
    "para_eng_counters = {}\n",
    "for lang in list(paragraph_dsets.keys()):\n",
    "    para_ind_counters[lang] = Counter()\n",
    "    para_eng_counters[lang] = Counter()\n",
    "    for word in ind_words:\n",
    "        para_ind_counters[lang][word] = paragraph_counters[lang][word]\n",
    "    for word in eng_words:\n",
    "        para_eng_counters[lang][word] = paragraph_counters[lang][word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for lang in list(paragraph_dsets.keys()):    \n",
    "    data = {'lang':[], 'word':[], 'count':[]}\n",
    "    for w, c in para_ind_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "        data['lang'].append(lang)\n",
    "    dfs.append(pd.DataFrame(data))\n",
    "pd.concat(dfs).to_csv(f'./para_counts/ind_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for lang in list(paragraph_dsets.keys()):    \n",
    "    data = {'lang':[], 'word':[], 'count':[]}\n",
    "    for w, c in para_eng_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "        data['lang'].append(lang)\n",
    "    dfs.append(pd.DataFrame(data))\n",
    "pd.concat(dfs).to_csv(f'./para_counts/eng_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Count\n",
    "mt_ind_counters = {}\n",
    "mt_eng_counters = {}\n",
    "for lang in list(mt_dsets.keys()):\n",
    "    mt_ind_counters[lang] = Counter()\n",
    "    mt_eng_counters[lang] = Counter()\n",
    "    for word in ind_words:\n",
    "        mt_ind_counters[lang][word] = mt_counters[lang][word]\n",
    "    for word in eng_words:\n",
    "        mt_eng_counters[lang][word] = mt_counters[lang][word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for lang in list(mt_dsets.keys()):    \n",
    "    data = {'lang':[], 'word':[], 'count':[]}\n",
    "    for w, c in mt_ind_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "        data['lang'].append(lang)\n",
    "    dfs.append(pd.DataFrame(data))\n",
    "pd.concat(dfs).to_csv(f'./mt_counts/ind_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for lang in list(mt_dsets.keys()):    \n",
    "    data = {'lang':[], 'word':[], 'count':[]}\n",
    "    for w, c in mt_eng_counters[lang].most_common(200):\n",
    "        data['word'].append(w)\n",
    "        data['count'].append(c)\n",
    "        data['lang'].append(lang)\n",
    "    dfs.append(pd.DataFrame(data))\n",
    "pd.concat(dfs).to_csv(f'./mt_counts/eng_200_count.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 9)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "corpus = [\n",
    "    'This is the first document.',\n",
    "    'This document is the second document.',\n",
    "    'And this is the third one.',\n",
    "    'Is this the first document?',\n",
    "]\n",
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "vectorizer.get_feature_names_out()\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [\n",
    "    'indomie', 'angkot', 'rendang', 'tempe', 'tahu',\n",
    "    'doa', 'ibadah', 'ojol', 'alay', 'gojek', 'jamu', \n",
    "    'gaib', 'santun', 'bule', 'alhamdulilah', 'insyaallah',\n",
    "    'korupsi', 'gule', 'soto', 'sate'\n",
    "]\n",
    "wiki_data = {'word':[], 'bug':[], 'jav':[], 'mad':[], 'min':[], 'sun':[]}\n",
    "para_data = {'word':[], 'bug':[], 'jav':[], 'mad':[], 'min':[], 'sun':[]}\n",
    "mt_data = {'word':[], 'jav':[], 'mad':[], 'min':[], 'sun':[]}\n",
    "for word in words:\n",
    "    wiki_data['word'].append(word)\n",
    "    para_data['word'].append(word)\n",
    "    mt_data['word'].append(word)\n",
    "    for lang in list(mt_dsets.keys()):\n",
    "        wiki_data[lang].append(wiki_counters[lang][word])\n",
    "        para_data[lang].append(paragraph_counters[lang][word])\n",
    "        mt_data[lang].append(mt_counters[lang][word])\n",
    "\n",
    "    for lang in ['bug']: \n",
    "        wiki_data[lang].append(wiki_counters[lang][word])\n",
    "        para_data[lang].append(paragraph_counters[lang][word])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>bug</th>\n",
       "      <th>jav</th>\n",
       "      <th>mad</th>\n",
       "      <th>min</th>\n",
       "      <th>sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>indomie</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>angkot</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rendang</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tempe</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tahu</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>5</td>\n",
       "      <td>125</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>doa</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibadah</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>4</td>\n",
       "      <td>159</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ojol</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alay</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gojek</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>jamu</td>\n",
       "      <td>0</td>\n",
       "      <td>521</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gaib</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>santun</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bule</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>alhamdulilah</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>insyaallah</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>korupsi</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gule</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>soto</td>\n",
       "      <td>12</td>\n",
       "      <td>365</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sate</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            word  bug  jav  mad  min  sun\n",
       "0        indomie    0   36    1    1   51\n",
       "1         angkot    0   14    0    8   56\n",
       "2        rendang    0   36    0   14   24\n",
       "3          tempe    0  123    0   29   11\n",
       "4           tahu    0  708    5  125  187\n",
       "5            doa    0   88    2   35   65\n",
       "6         ibadah    0  124    4  159  263\n",
       "7           ojol    0    0    0    0    0\n",
       "8           alay    0    7    0    0    0\n",
       "9          gojek    0   18    1   20    0\n",
       "10          jamu    0  521    1   17   32\n",
       "11          gaib    0   90    0   17   70\n",
       "12        santun    0   39    0    7   14\n",
       "13          bule    0   19    0    5    7\n",
       "14  alhamdulilah    0    2    0    0    0\n",
       "15    insyaallah    0    2    0    0    0\n",
       "16       korupsi    0  190   11   85   41\n",
       "17          gule    0   26    0    2    4\n",
       "18          soto   12  365    4   66  328\n",
       "19          sate    0  223    2  169   21"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(wiki_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>bug</th>\n",
       "      <th>jav</th>\n",
       "      <th>mad</th>\n",
       "      <th>min</th>\n",
       "      <th>sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>indomie</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>angkot</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rendang</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tempe</td>\n",
       "      <td>2</td>\n",
       "      <td>101</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tahu</td>\n",
       "      <td>32</td>\n",
       "      <td>117</td>\n",
       "      <td>87</td>\n",
       "      <td>127</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>doa</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibadah</td>\n",
       "      <td>33</td>\n",
       "      <td>195</td>\n",
       "      <td>32</td>\n",
       "      <td>139</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ojol</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alay</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gojek</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>jamu</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gaib</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>santun</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bule</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>alhamdulilah</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>insyaallah</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>korupsi</td>\n",
       "      <td>4</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gule</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>soto</td>\n",
       "      <td>5</td>\n",
       "      <td>98</td>\n",
       "      <td>54</td>\n",
       "      <td>68</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sate</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>122</td>\n",
       "      <td>112</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            word  bug  jav  mad  min  sun\n",
       "0        indomie    0   24    9   12   15\n",
       "1         angkot    8   52    3   57  254\n",
       "2        rendang    7   27    9    4   32\n",
       "3          tempe    2  101   45   29   31\n",
       "4           tahu   32  117   87  127  128\n",
       "5            doa   11    5    3  102   82\n",
       "6         ibadah   33  195   32  139  240\n",
       "7           ojol    0    6    0    0   16\n",
       "8           alay    0    2    3    9    1\n",
       "9          gojek   12   15   15    4   17\n",
       "10          jamu    0   61    0   16   73\n",
       "11          gaib    0   46    1   13   18\n",
       "12        santun    0    9    5   11   14\n",
       "13          bule    1    5    8    1    9\n",
       "14  alhamdulilah    0    1    6    2   24\n",
       "15    insyaallah    0    1   12   17    8\n",
       "16       korupsi    4   76   12   21   46\n",
       "17          gule    0    8   32    0    2\n",
       "18          soto    5   98   54   68   34\n",
       "19          sate    7   69  122  112   91"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(para_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>jav</th>\n",
       "      <th>mad</th>\n",
       "      <th>min</th>\n",
       "      <th>sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>indomie</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>angkot</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rendang</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tempe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tahu</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>doa</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibadah</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ojol</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alay</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gojek</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>jamu</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gaib</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>santun</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bule</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>alhamdulilah</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>insyaallah</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>korupsi</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gule</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>soto</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sate</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            word  jav  mad  min  sun\n",
       "0        indomie    9    7   11   11\n",
       "1         angkot    6    4    6    5\n",
       "2        rendang    3    3    0    3\n",
       "3          tempe    3    2    2    3\n",
       "4           tahu   28    6   26    6\n",
       "5            doa    1   12   47   36\n",
       "6         ibadah   12    2   15   16\n",
       "7           ojol    5    4    6    5\n",
       "8           alay    9    9   11    7\n",
       "9          gojek   15   13   15   14\n",
       "10          jamu    1    0    1    0\n",
       "11          gaib    3    2    1    2\n",
       "12        santun    2    0    2    3\n",
       "13          bule    9    8   16   15\n",
       "14  alhamdulilah    2    4    2   14\n",
       "15    insyaallah    4    4    2    1\n",
       "16       korupsi   33   33   29   29\n",
       "17          gule    0    0    0    0\n",
       "18          soto    1    1    1    1\n",
       "19          sate    3    3    3    3"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(mt_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(wiki_data).to_csv('wiki_common.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(para_data).to_csv('para_common.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(mt_data).to_csv('mt_common.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lexical Diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running On Wikipedia\n",
      "== jav ==\n",
      "WIKI: 0.8770643948054657 68.1535211554773 0.018800924337848423 0.8818755553851829 0.8798791771630815\n",
      "PARA: 0.8893802893316376 62.550863690913225 0.021392573223002996 0.8794541265391201 0.887571201987059\n",
      "MT: 0.9153986876852744 92.18846804381735 0.011737639116165089 0.9155835205906315 0.913349573395385\n",
      "Running On Wikipedia\n",
      "Running On Paragraph\n",
      "Running On MT\n",
      "== mad ==\n",
      "WIKI: 0.8867065172883336 89.46325483913797 0.01461384815239539 0.9053390097531582 0.8851669432447673\n",
      "PARA: 0.9077131181144935 78.25656188698593 0.018553149951451076 0.900285721030389 0.9063579485398714\n",
      "MT: 0.9113144673306145 88.60186707467294 0.012359094916752864 0.9112207169901037 0.91011012252042\n",
      "Running On Wikipedia\n",
      "Running On Paragraph\n",
      "Running On MT\n",
      "== bug ==\n",
      "WIKI: 0.8025172093314292 51.02237303259746 0.02666852624042112 0.8073381572675332 0.8232195875373821\n",
      "PARA: 0.90198554467218 82.78228985188375 0.017448242734691838 0.8963594027872709 0.8999351785714286\n",
      "Running On Wikipedia\n",
      "Running On Paragraph\n",
      "Running On MT\n",
      "== min ==\n",
      "WIKI: 0.9346951572627828 89.5601696092024 0.011843944243683359 0.9211582735675593 0.9231144221384834\n",
      "PARA: 0.9063460590367063 78.58110590660242 0.018383250620564597 0.8996266605959125 0.9047705061099578\n",
      "MT: 0.9109823365895461 89.63550982229371 0.012259036405753041 0.9118589721166855 0.9094445235383446\n",
      "Running On Wikipedia\n",
      "Running On Paragraph\n",
      "Running On MT\n",
      "== sun ==\n",
      "WIKI: 0.9113387836041631 74.85608594348213 0.01684790543462043 0.8892273829602992 0.9022421833377625\n",
      "PARA: 0.9096169406793658 85.59911560109647 0.017578251363307696 0.9057675676567516 0.9081058346200622\n",
      "MT: 0.9101912614867678 87.93074852891844 0.012532285707630519 0.9095875806605 0.9071161210032549\n"
     ]
    }
   ],
   "source": [
    "from lexicalrichness import LexicalRichness\n",
    "\n",
    "for lang in ['jav', 'mad', 'bug', 'min', 'sun']:\n",
    "    # Clean Text\n",
    "    replacement_rules = str.maketrans('', '', string.punctuation + '0123456789')\n",
    "    wiki_dsets[lang]['clean_text'] = wiki_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_dsets[lang]['clean_text'] = paragraph_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in mt_dsets:\n",
    "        mt_dsets[lang]['clean_text'] = mt_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "\n",
    "    print('Running On Wikipedia')\n",
    "    mattrs, mtlds, maass, hdds, msttrs = [], [], [], [], []\n",
    "    for i, text in enumerate(wiki_dsets[lang]['clean_text']):\n",
    "        lex = LexicalRichness(text)\n",
    "        if len(list(filter(lambda x: len(x) > 1, text.strip().split(' ')))) >= 25:\n",
    "            mattrs.append(lex.mattr(window_size=20))\n",
    "            mtlds.append(lex.mtld(threshold=0.72))\n",
    "            maass.append(lex.Maas)\n",
    "            hdds.append(lex.hdd(draws=20))\n",
    "            # vocds.append(lex.vocd(ntokens=50, within_sample=100, iterations=3))\n",
    "            msttrs.append(lex.msttr(segment_window=20))\n",
    "        else:\n",
    "            mattrs.append(np.nan)\n",
    "            mtlds.append(np.nan)\n",
    "            maass.append(np.nan)\n",
    "            hdds.append(np.nan)\n",
    "            msttrs.append(np.nan)\n",
    "    wiki_dsets[lang]['mattr'] = mattrs\n",
    "    wiki_dsets[lang]['mtld'] = mtlds\n",
    "    wiki_dsets[lang]['maas'] = maass\n",
    "    wiki_dsets[lang]['hdd'] = hdds\n",
    "    wiki_dsets[lang]['msttr'] = msttrs\n",
    "        \n",
    "    if lang != 'jav':\n",
    "        print('Running On Paragraph')\n",
    "        if lang in paragraph_dsets:\n",
    "            mattrs, mtlds, maass, hdds, msttrs = [], [], [], [], []\n",
    "            for i, text in enumerate(paragraph_dsets[lang]['clean_text']):\n",
    "                lex = LexicalRichness(text)\n",
    "                if len(list(filter(lambda x: len(x) > 1, text.strip().split(' ')))) >= 25:\n",
    "                    mattrs.append(lex.mattr(window_size=20))\n",
    "                    mtlds.append(lex.mtld(threshold=0.72))\n",
    "                    maass.append(lex.Maas)\n",
    "                    hdds.append(lex.hdd(draws=20))\n",
    "                    msttrs.append(lex.msttr(segment_window=20))\n",
    "                else:\n",
    "                    mattrs.append(np.nan)\n",
    "                    mtlds.append(np.nan)\n",
    "                    maass.append(np.nan)\n",
    "                    hdds.append(np.nan)\n",
    "                    msttrs.append(np.nan)\n",
    "            paragraph_dsets[lang]['mattr'] = mattrs\n",
    "            paragraph_dsets[lang]['mtld'] = mtlds\n",
    "            paragraph_dsets[lang]['maas'] = maass\n",
    "            paragraph_dsets[lang]['hdd'] = hdds\n",
    "            paragraph_dsets[lang]['msttr'] = msttrs\n",
    "\n",
    "        print('Running On MT')\n",
    "        if lang in mt_dsets:\n",
    "            mattrs, mtlds, maass, hdds, msttrs = [], [], [], [], []\n",
    "            for i, text in enumerate(mt_dsets[lang]['clean_text']):\n",
    "                lex = LexicalRichness(text)\n",
    "                if len(list(filter(lambda x: len(x) > 1, text.strip().split(' ')))) >= 25:\n",
    "                    mattrs.append(lex.mattr(window_size=20))\n",
    "                    mtlds.append(lex.mtld(threshold=0.72))\n",
    "                    maass.append(lex.Maas)\n",
    "                    hdds.append(lex.hdd(draws=20))\n",
    "                    msttrs.append(lex.msttr(segment_window=20))\n",
    "                else:\n",
    "                    mattrs.append(np.nan)\n",
    "                    mtlds.append(np.nan)\n",
    "                    maass.append(np.nan)\n",
    "                    hdds.append(np.nan)\n",
    "                    msttrs.append(np.nan)\n",
    "            mt_dsets[lang]['mattr'] = mattrs\n",
    "            mt_dsets[lang]['mtld'] = mtlds\n",
    "            mt_dsets[lang]['maas'] = maass\n",
    "            mt_dsets[lang]['hdd'] = hdds\n",
    "            mt_dsets[lang]['msttr'] = msttrs\n",
    "    \n",
    "#     wiki_dsets[lang][['text', 'mattr', 'mtld', 'maas']].to_csv(f'diversity/wiki_{lang}.csv')\n",
    "#     if lang in paragraph_dsets:\n",
    "#         paragraph_dsets[lang][['text', 'mattr', 'mtld', 'maas']].to_csv(f'diversity/para_{lang}.csv')\n",
    "#     if lang in mt_dsets:\n",
    "#         mt_dsets[lang][['text', 'mattr', 'mtld', 'maas']].to_csv(f'diversity/mt_{lang}.csv')\n",
    "    \n",
    "    print(f'== {lang} ==')\n",
    "    print(f'WIKI: {wiki_dsets[lang][\"mattr\"].mean()} {wiki_dsets[lang][\"mtld\"].mean()} {wiki_dsets[lang][\"maas\"].mean()} {wiki_dsets[lang][\"hdd\"].mean()} {wiki_dsets[lang][\"msttr\"].mean()}')\n",
    "    if lang in paragraph_dsets:\n",
    "        print(f'PARA: {paragraph_dsets[lang][\"mattr\"].mean()} {paragraph_dsets[lang][\"mtld\"].mean()} {paragraph_dsets[lang][\"maas\"].mean()} {paragraph_dsets[lang][\"hdd\"].mean()} {paragraph_dsets[lang][\"msttr\"].mean()}')\n",
    "    if lang in mt_dsets:\n",
    "        print(f'MT: {mt_dsets[lang][\"mattr\"].mean()} {mt_dsets[lang][\"mtld\"].mean()} {mt_dsets[lang][\"maas\"].mean()} {mt_dsets[lang][\"hdd\"].mean()} {mt_dsets[lang][\"msttr\"].mean()}')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== bug ==\n",
      "WIKI: 0.8025172093314292 51.02237303259746 0.02666852624042112 0.8073381572675332 0.8232195875373821\n",
      "PARA: 0.90198554467218 82.78228985188375 0.017448242734691838 0.8963594027872709 0.8999351785714286\n",
      "== mad ==\n",
      "WIKI: 0.8867065172883336 89.46325483913797 0.01461384815239539 0.9053390097531582 0.8851669432447673\n",
      "PARA: 0.9077131181144935 78.25656188698593 0.018553149951451076 0.900285721030389 0.9063579485398714\n",
      "MT: 0.9113144673306145 88.60186707467294 0.012359094916752864 0.9112207169901037 0.91011012252042\n",
      "== jav ==\n",
      "WIKI: 0.8770643948054657 68.1535211554773 0.018800924337848423 0.8818755553851829 0.8798791771630815\n",
      "PARA: 0.8893802893316376 62.550863690913225 0.021392573223002996 0.8794541265391201 0.887571201987059\n",
      "MT: 0.9153986876852744 92.18846804381735 0.011737639116165089 0.9155835205906315 0.913349573395385\n",
      "== min ==\n",
      "WIKI: 0.9346951572627828 89.5601696092024 0.011843944243683359 0.9211582735675593 0.9231144221384834\n",
      "PARA: 0.9063460590367063 78.58110590660242 0.018383250620564597 0.8996266605959125 0.9047705061099578\n",
      "MT: 0.9109823365895461 89.63550982229371 0.012259036405753041 0.9118589721166855 0.9094445235383446\n",
      "== sun ==\n",
      "WIKI: 0.9113387836041631 74.85608594348213 0.01684790543462043 0.8892273829602992 0.9022421833377625\n",
      "PARA: 0.9096169406793658 85.59911560109647 0.017578251363307696 0.9057675676567516 0.9081058346200622\n",
      "MT: 0.9101912614867678 87.93074852891844 0.012532285707630519 0.9095875806605 0.9071161210032549\n"
     ]
    }
   ],
   "source": [
    "for lang in ['bug', 'mad', 'jav', 'min', 'sun']:\n",
    "    print(f'== {lang} ==')\n",
    "    print(f'WIKI: {wiki_dsets[lang][\"mattr\"].mean()} {wiki_dsets[lang][\"mtld\"].mean()} {wiki_dsets[lang][\"maas\"].mean()} {wiki_dsets[lang][\"hdd\"].mean()} {wiki_dsets[lang][\"msttr\"].mean()}')\n",
    "    if lang in paragraph_dsets:\n",
    "        print(f'PARA: {paragraph_dsets[lang][\"mattr\"].mean()} {paragraph_dsets[lang][\"mtld\"].mean()} {paragraph_dsets[lang][\"maas\"].mean()} {paragraph_dsets[lang][\"hdd\"].mean()} {paragraph_dsets[lang][\"msttr\"].mean()}')\n",
    "    if lang in mt_dsets:\n",
    "        print(f'MT: {mt_dsets[lang][\"mattr\"].mean()} {mt_dsets[lang][\"mtld\"].mean()} {mt_dsets[lang][\"maas\"].mean()} {mt_dsets[lang][\"hdd\"].mean()} {mt_dsets[lang][\"msttr\"].mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== bug ==\n",
      "WIKI: 0.798076923076923 28.60677612538577 0.026552829810731517 0.801241125596174 0.85\n",
      "PARA: 0.9051178524661724 75.99401102524992 0.01731726241365138 0.8987034675079688 0.9\n",
      "== mad ==\n",
      "WIKI: 0.8988140161725064 67.20860273523544 0.014337578918395131 0.9105786982309678 0.9\n",
      "PARA: 0.9112244897959195 73.29579525503084 0.0183536821752621 0.9029199023029529 0.9099999999999999\n",
      "MT: 0.9222222222222223 71.48752709577528 0.011117123943966505 0.9217503217503209 0.925\n",
      "== jav ==\n",
      "WIKI: 0.8917724203089938 58.936399932042136 0.017942453849145175 0.8974366571235914 0.8926198257080609\n",
      "PARA: 0.8933734939759037 58.154803379203855 0.02105534115542086 0.8839220409108955 0.89\n",
      "MT: 0.9262531328320802 72.57600000000004 0.010843046712604575 0.9246031746031743 0.925\n",
      "== min ==\n",
      "WIKI: 0.9423076923076922 80.86400000000003 0.011127399755762301 0.9242495309568486 0.925\n",
      "PARA: 0.9095916666666675 73.36580305173143 0.01818511374491436 0.9024878767852461 0.9099999999999999\n",
      "MT: 0.9215838509316774 68.72444444444443 0.011117123943966505 0.9217090010315492 0.925\n",
      "== sun ==\n",
      "WIKI: 0.9447368421052632 70.1199043519847 0.013500000229674274 0.9025917506228567 0.9166666666666666\n",
      "PARA: 0.9128981537837302 79.89629452512722 0.017319486696368736 0.9085187111024309 0.9099999999999999\n",
      "MT: 0.9214285714285715 68.72444444444443 0.011134675708470104 0.9194580259797652 0.925\n"
     ]
    }
   ],
   "source": [
    "for lang in ['bug', 'mad', 'jav', 'min', 'sun']:\n",
    "    print(f'== {lang} ==')\n",
    "    print(f'WIKI: {wiki_dsets[lang][\"mattr\"].median()} {wiki_dsets[lang][\"mtld\"].median()} {wiki_dsets[lang][\"maas\"].median()} {wiki_dsets[lang][\"hdd\"].median()} {wiki_dsets[lang][\"msttr\"].median()}')\n",
    "    if lang in paragraph_dsets:\n",
    "        print(f'PARA: {paragraph_dsets[lang][\"mattr\"].median()} {paragraph_dsets[lang][\"mtld\"].median()} {paragraph_dsets[lang][\"maas\"].median()} {paragraph_dsets[lang][\"hdd\"].median()} {paragraph_dsets[lang][\"msttr\"].median()}')\n",
    "    if lang in mt_dsets:\n",
    "        print(f'MT: {mt_dsets[lang][\"mattr\"].median()} {mt_dsets[lang][\"mtld\"].median()} {mt_dsets[lang][\"maas\"].median()} {mt_dsets[lang][\"hdd\"].median()} {mt_dsets[lang][\"msttr\"].median()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== bug ==\n",
      "WIKI: 0.8025172093314292 51.02237303259746 0.02666852624042112 0.8073381572675332 0.8232195875373821\n",
      "== mad ==\n",
      "WIKI: 0.8867065172883336 89.46325483913797 0.01461384815239539 0.9053390097531582 0.8851669432447673\n",
      "== jav ==\n",
      "WIKI: 0.8770643948054657 68.1535211554773 0.018800924337848423 0.8818755553851829 0.8798791771630815\n",
      "== min ==\n",
      "WIKI: 0.9346951572627828 89.5601696092024 0.011843944243683359 0.9211582735675593 0.9231144221384834\n",
      "== sun ==\n",
      "WIKI: 0.9113387836041631 74.85608594348213 0.01684790543462043 0.8892273829602992 0.9022421833377625\n",
      "== map-bms ==\n",
      "WIKI: 0.8661854005731374 70.76320693550588 0.019062702037850775 0.8789127996416582 0.8617453113836908\n",
      "== ace ==\n",
      "WIKI: 0.7787468003710114 30.653040031302027 0.03446295026505121 0.7591176274019195 0.8017896065652931\n"
     ]
    }
   ],
   "source": [
    "from lexicalrichness import LexicalRichness\n",
    "\n",
    "for lang in list(wiki_dsets.keys()):\n",
    "    # Clean Text\n",
    "    replacement_rules = str.maketrans('', '', string.punctuation + '0123456789')\n",
    "    wiki_dsets[lang]['clean_text'] = wiki_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in paragraph_dsets:\n",
    "        paragraph_dsets[lang]['clean_text'] = paragraph_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "    if lang in mt_dsets:\n",
    "        mt_dsets[lang]['clean_text'] = mt_dsets[lang]['text'].apply(lambda x: x.lower().translate(replacement_rules).replace('\\n',' '))\n",
    "\n",
    "    mattrs, mtlds, maass, hdds, msttrs = [], [], [], [], []\n",
    "    for i, text in enumerate(wiki_dsets[lang]['clean_text']):\n",
    "        lex = LexicalRichness(text)\n",
    "        if len(list(filter(lambda x: len(x) > 1, text.strip().split(' ')))) >= 25:\n",
    "            mattrs.append(lex.mattr(window_size=20))\n",
    "            mtlds.append(lex.mtld(threshold=0.72))\n",
    "            maass.append(lex.Maas)\n",
    "            hdds.append(lex.hdd(draws=20))\n",
    "            # vocds.append(lex.vocd(ntokens=25, within_sample=100, iterations=3))\n",
    "            msttrs.append(lex.msttr(segment_window=20))\n",
    "        else:\n",
    "            mattrs.append(np.nan)\n",
    "            mtlds.append(np.nan)\n",
    "            maass.append(np.nan)\n",
    "            hdds.append(np.nan)\n",
    "            msttrs.append(np.nan)\n",
    "    wiki_dsets[lang]['mattr'] = mattrs\n",
    "    wiki_dsets[lang]['mtld'] = mtlds\n",
    "    wiki_dsets[lang]['maas'] = maass\n",
    "    wiki_dsets[lang]['hdd'] = hdds\n",
    "    wiki_dsets[lang]['msttr'] = msttrs\n",
    "    \n",
    "    print(f'== {lang} ==')\n",
    "    print(f'WIKI: {wiki_dsets[lang][\"mattr\"].mean()} {wiki_dsets[lang][\"mtld\"].mean()} {wiki_dsets[lang][\"maas\"].mean()} {wiki_dsets[lang][\"hdd\"].mean()} {wiki_dsets[lang][\"msttr\"].mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bug WIKI 15866 0.8025172093314292 51.02237303259746 0.02666852624042112 0.8073381572675332 0.8232195875373821\n",
      "mad WIKI 1002 0.8867065172883336 89.46325483913797 0.01461384815239539 0.9053390097531582 0.8851669432447673\n",
      "jav WIKI 72737 0.8770643948054657 68.1535211554773 0.018800924337848423 0.8818755553851829 0.8798791771630815\n",
      "min WIKI 226237 0.9346951572627828 89.5601696092024 0.011843944243683359 0.9211582735675593 0.9231144221384834\n",
      "sun WIKI 61331 0.9113387836041631 74.85608594348213 0.01684790543462043 0.8892273829602992 0.9022421833377625\n",
      "map-bms WIKI 13575 0.8661854005731374 70.76320693550588 0.019062702037850775 0.8789127996416582 0.8617453113836908\n",
      "ace WIKI 12829 0.7787468003710114 30.653040031302027 0.03446295026505121 0.7591176274019195 0.8017896065652931\n",
      "gor WIKI 14389 0.6940185419158188 37.225365925589436 0.042645348260039385 0.7135722061881534 0.8202527344064151\n",
      "ban WIKI 17161 0.8515263409214967 53.8335470548051 0.021629884329270658 0.865722282832719 0.8650753408056899\n",
      "bjn WIKI 10122 0.8726883575520812 83.56524004355468 0.01853124183418452 0.8820441065971484 0.867875776617603\n",
      "nia WIKI 1620 0.8475030221256692 68.84886849217023 0.021214668768093307 0.8632540219703946 0.8472916613516343\n",
      "ind WIKI 633703 0.8987789313583968 83.81833923821242 0.01722899256816845 0.901059633718551 0.8991739820639923\n"
     ]
    }
   ],
   "source": [
    "from lexicalrichness import LexicalRichness\n",
    "\n",
    "for lang in list(wiki_dsets.keys()):\n",
    "    print(f'{lang} WIKI {len(wiki_dsets[lang])} {wiki_dsets[lang][\"mattr\"].mean()} {wiki_dsets[lang][\"mtld\"].mean()} {wiki_dsets[lang][\"maas\"].mean()} {wiki_dsets[lang][\"hdd\"].mean()} {wiki_dsets[lang][\"msttr\"].mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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